Generative AI Architect

Position: Generative AI Architect

Location: Allentown PA- Hybrid

Generative AI Architect

The Generative AI Architect will be responsible for designing, developing, and implementing generative AI solutions that align with the company's strategic objectives. This role involves leading the architecture and deployment of advanced AI models, ensuring scalability, security, and ethical considerations are integrated into all AI initiatives. The ideal candidate will possess deep expertise in generative AI technologies, a strong understanding of AI ethics, and the ability to collaborate effectively with cross-functional teams.

Key Responsibilities

• Architecture Design: Develop and maintain the architectural framework for generative AI solutions, ensuring alignment with business goals and technical standards.
• Model Development: Lead the design, training, and deployment of generative AI models (e.g., GPT, DALL-E, Stable Diffusion) tailored to various applications such as content generation, data synthesis, and automation.
• Integration: Collaborate with software engineering teams to integrate generative AI capabilities into existing systems and workflows.
• Scalability & Performance: Ensure AI solutions are scalable, efficient, and optimized for performance across different platforms and environments.
• Ethical AI Practices: Implement and enforce ethical guidelines for AI development and deployment, addressing issues such as bias, fairness, and transparency.
• Research & Innovation: Stay abreast of the latest advancements in generative AI and related fields, incorporating new techniques and tools into the company's AI strategy.
• Collaboration: Work closely with data scientists, engineers, product managers, and other stakeholders to identify opportunities for AI-driven solutions and ensure successful project delivery.
• Documentation & Standards: Create comprehensive documentation for AI architectures, processes, and best practices. Establish and maintain coding and architectural standards.
• Security: Ensure that generative AI systems comply with security protocols and data protection regulations.
• Mentorship: Provide guidance and mentorship to junior team members and foster a culture of continuous learning within the AI team.

Qualifications

Education:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. A Ph.D. is a plus.

Experience:

• Minimum of 5 years of experience in AI/ML architecture or a related role.
• Proven experience in designing and deploying generative AI models and solutions.
• Hands-on experience with AI frameworks and tools such as TensorFlow, PyTorch, Hugging Face, etc.
• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and deploying AI solutions in cloud environments.

Technical Skills:

• Proficiency in programming languages such as Python, Java, or C++.
• Strong understanding of machine learning algorithms, deep learning architectures, and natural language processing.
• Familiarity with data preprocessing, feature engineering, and model evaluation techniques.
• Knowledge of API development and integration.
• Experience with containerization and orchestration tools like Docker and Kubernetes.

Soft Skills:

• Excellent problem-solving and analytical abilities.
• Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

"MUST HAVE" Skills

• Experienced and proven track record with analyzing GenAI requirements and proposing GenAI architecture solution that applies to use case and available data. Preferably using resources available in Azure.
• Experience with data sources used for GenAI ingestion like Sharepoint Online, Azure Blob Storage, Snowflake, Databricks.
• Experience with Microsoft 365 Copilot and Custom Copilots.
• Must possess expertise in prompt engineering
• Knowledge and experience with latest GenAI solutions, practices and standards,
• Must possess strong communication skills to be able to convey the intricacies of current GenAI architecture solutions to different audiences.
• Knowledge and experience with GenAI testing processes and implementation
• Knowledge and experience with latest GenAI "responsible AI" tools and implementation.
• A tactical AND strategic mindset to delivering GenAI solution architectures so they can be easily adapted as the technology evolves.